This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Ce cours fait partie de la Spécialisation Statistics with R
Introduction to Probability and Data
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À propos de ce cours
275,262 consultations récentes
Résultats de carrière des étudiants
33%
ont commencé une nouvelle carrière après avoir terminé ce cours
30%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Compétences que vous acquerrez
StatisticsR ProgrammingRstudioExploratory Data Analysis
Résultats de carrière des étudiants
33%
ont commencé une nouvelle carrière après avoir terminé ce cours
30%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
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Niveau débutant
Approx. 22 heures pour terminer
Recommandé : 5 weeks of study, 5-7 hours/week
Anglais
Sous-titres : Anglais, Coréen
Offert par
Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Programme du cours : ce que vous apprendrez dans ce cours
12 minutes pour terminer
About Introduction to Probability and Data
This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.
12 minutes pour terminer
1 vidéo (Total 2 min), 1 lecture
1 vidéo
1 lecture
More about Introduction to Probability and Data10 min
1 heure pour terminer
Introduction to Data
Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.
1 heure pour terminer
6 vidéos (Total 28 min), 2 lectures, 2 quiz
6 vidéos
Introduction3 min
Data Basics5 min
Observational Studies & Experiments4 min
Sampling and sources of bias8 min
Experimental Design2 min
(Spotlight) Random Sample Assignment3 min
2 lectures
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 1 Practice Quiz10 min
Week 1 Quiz14 min
1 heure pour terminer
Introduction to Data Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 heure pour terminer
2 lectures
2 lectures
About Lab Choices (Read Before Selection)10 min
Week 1 Lab Instructions (RStudio)10 min
1 exercice pour s'entraîner
Week 1 Lab: Introduction to R and RStudio16 min
2 heures pour terminer
Exploratory Data Analysis and Introduction to Inference
Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1. This week we will delve into numerical and categorical data in more depth, and introduce inference.
2 heures pour terminer
7 vidéos (Total 46 min), 3 lectures, 2 quiz
7 vidéos
Measures of Center4 min
Measures of Spread6 min
Robust Statistics1 min
Transforming Data3 min
Exploring Categorical Variables8 min
Introduction to Inference12 min
3 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 2 Practice Quiz10 min
Week 2 Quiz12 min
1 heure pour terminer
Exploratory Data Analysis and Introduction to Inference Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 heure pour terminer
2 lectures
2 lectures
Week 2 Lab Instructions (RStudio)10 min
Week 2 Lab Instructions (RStudio Cloud)10 min
1 exercice pour s'entraîner
Week 2 Lab: Introduction to Data20 min
2 heures pour terminer
Introduction to Probability
Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. This week we will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. Thank you for your enthusiasm and participation, and have a great week! I’m looking forward to working with you on the rest of this course.
2 heures pour terminer
9 vidéos (Total 82 min), 3 lectures, 2 quiz
9 vidéos
Introduction5 min
Disjoint Events + General Addition Rule9 min
Independence9 min
Probability Examples9 min
(Spotlight) Disjoint vs. Independent2 min
Conditional Probability12 min
Probability Trees10 min
Bayesian Inference14 min
Examples of Bayesian Inference7 min
3 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 3 Practice Quiz6 min
Week 3 Quiz10 min
1 heure pour terminer
Introduction to Probability Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1 heure pour terminer
2 lectures
2 lectures
Week 3 Lab Instructions (RStudio)10 min
Week 3 Lab Instructions (RStudio Cloud)10 min
1 exercice pour s'entraîner
Week 3 Lab: Probability10 min
2 heures pour terminer
Probability Distributions
Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be no labs for this week. Please don't hesitate to post any questions, discussions and related topics on this week's forum (https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1).
2 heures pour terminer
6 vidéos (Total 67 min), 4 lectures, 2 quiz
6 vidéos
Normal Distribution17 min
Evaluating the Normal Distribution2 min
Working with the Normal Distribution5 min
Binomial Distribution17 min
Normal Approximation to Binomial14 min
Working with the Binomial Distribution9 min
4 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
Data Analysis Project Example10 min
2 exercices pour s'entraîner
Week 4 Practice Quiz14 min
Week 4 Quiz14 min
Avis
4.7
Meilleurs avis pour INTRODUCTION TO PROBABILITY AND DATA
par AAJan 23, 2018
This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.
par BBSep 3, 2019
Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.
par MMJul 5, 2019
Good but questions lacked clarity in what is expected. i.e. "Work out the Boy to Girl ratio" - in what fashion do we do this, as it appeared to be the same as simply working out the "proportion of b
par AAJun 9, 2017
A good introductory course to data analysis, statistics and the R programming language. Recommended to people who are new to data analysis and also those who are experienced and could use a refresh.
par HDMar 30, 2018
The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.
par HWDec 13, 2017
The lectures were very clear and concise and the examples were very relevant. Some of the R instructions left a little something to be desired, but nothing a little time and google couldn't solve.
par RRSep 17, 2016
Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher
par SSJul 26, 2017
Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.
À propos du Spécialisation Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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